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https://git.datalinker.icu/vllm-project/vllm.git
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b539222d4e
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Roger Wang <ywang@roblox.com> Co-authored-by: Roger Wang <ywang@roblox.com>
312 lines
13 KiB
Python
312 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import time
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from collections.abc import Mapping
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from typing import Optional, Union
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import vllm.platforms
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from vllm.config import VllmConfig
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from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
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PromptType, SingletonInputsAdapter)
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from vllm.inputs.parse import is_encoder_decoder_inputs
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from vllm.inputs.preprocess import InputPreprocessor
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from vllm.lora.request import LoRARequest
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from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
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MultiModalRegistry)
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from vllm.multimodal.inputs import PlaceholderRange
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from vllm.multimodal.utils import merge_and_sort_multimodal_metadata
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from vllm.pooling_params import PoolingParams
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from vllm.prompt_adapter.request import PromptAdapterRequest
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from vllm.sampling_params import SamplingParams
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from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.structured_output.utils import validate_structured_output_request
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class Processor:
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def __init__(
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self,
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vllm_config: VllmConfig,
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tokenizer: BaseTokenizerGroup,
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input_registry: InputRegistry = INPUT_REGISTRY,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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):
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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config
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self.cache_config = vllm_config.cache_config
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self.lora_config = vllm_config.lora_config
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self.decoding_config = vllm_config.decoding_config
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self.tokenizer = tokenizer
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self.generation_config_fields = (
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self.model_config.try_get_generation_config())
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self.input_preprocessor = InputPreprocessor(self.model_config,
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self.tokenizer,
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mm_registry)
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# Multi-modal hasher (for images)
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self.use_hash = (
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not self.model_config.disable_mm_preprocessor_cache) or \
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self.cache_config.enable_prefix_caching
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def _validate_logprobs(
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self,
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params: SamplingParams,
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) -> None:
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max_logprobs = self.model_config.max_logprobs
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# Validate sample logprobs.
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if params.logprobs and params.logprobs > max_logprobs:
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raise ValueError(
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f"Requested sample logprobs of {params.logprobs}, "
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f"which is greater than max allowed: {max_logprobs}")
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# Validate prompt logprobs.
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if params.prompt_logprobs and params.prompt_logprobs > max_logprobs:
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raise ValueError(
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f"Requested prompt logprobs of {params.prompt_logprobs}, "
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f"which is greater than max allowed: {max_logprobs}")
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def _validate_sampling_params(
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self,
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params: SamplingParams,
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) -> None:
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self._validate_structured_output(params)
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if params.allowed_token_ids is None:
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return
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if not params.allowed_token_ids:
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raise ValueError("allowed_token_ids is not None and empty!")
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vocab_size = self.model_config.get_vocab_size()
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if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
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raise ValueError(
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"allowed_token_ids contains out-of-vocab token id!")
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def _validate_supported_sampling_params(
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self,
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params: SamplingParams,
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) -> None:
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# Best of not yet supported.
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if params.best_of is not None and params.best_of > 1:
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raise ValueError("vLLM V1 does not yet support best_of.")
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# Logits processors not supported.
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if params.logits_processors:
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raise ValueError("vLLM V1 does not support per request "
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"user provided logits processors.")
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def _validate_params(
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self,
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params: Union[SamplingParams, PoolingParams],
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):
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"""
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Validate supported SamplingParam.
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Should raise ValueError if unsupported for API Server.
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"""
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if not isinstance(params, SamplingParams):
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raise ValueError("V1 does not yet support Pooling models.")
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self._validate_logprobs(params)
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self._validate_sampling_params(params)
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self._validate_supported_sampling_params(params)
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def _validate_lora(self, lora_request: Optional[LoRARequest]) -> None:
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if lora_request is not None and not self.lora_config:
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raise ValueError(f"Got lora_request {lora_request} but LoRA is "
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"not enabled!")
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def _validate_structured_output(self, params: SamplingParams) -> None:
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if not params.guided_decoding or not self.decoding_config:
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return
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if self.decoding_config.guided_decoding_backend != "xgrammar":
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raise ValueError(
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"Only xgrammar structured output is supported in V1.")
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if (params.guided_decoding.backend
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and params.guided_decoding.backend != 'xgrammar'):
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raise ValueError(
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"Only xgrammar structured output is supported in V1.")
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if self.vllm_config.speculative_config:
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raise ValueError("Structured output is not supported with "
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"speculative decoding.")
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if vllm.platforms.current_platform.is_tpu():
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raise ValueError("Structured output is not supported on TPU.")
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validate_structured_output_request(params)
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def process_inputs(
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self,
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request_id: str,
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prompt: PromptType,
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params: Union[SamplingParams, PoolingParams],
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arrival_time: Optional[float] = None,
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lora_request: Optional[LoRARequest] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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prompt_adapter_request: Optional[PromptAdapterRequest] = None,
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priority: int = 0,
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) -> EngineCoreRequest:
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# TODO(woosuk): Support pooling models.
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# TODO(woosuk): Support encoder-decoder models.
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self._validate_lora(lora_request)
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self._validate_params(params)
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if priority != 0:
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raise ValueError("V1 does not support priority yet.")
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if trace_headers is not None:
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raise ValueError("V1 does not support tracing yet.")
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if prompt_adapter_request is not None:
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raise ValueError("V1 does not support prompt_adapter_request.")
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if arrival_time is None:
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arrival_time = time.time()
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# Process inputs, which includes:
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# 1. Tokenize text prompt, with LoRA request if one exists.
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# 2. For multimodal models with a merged preprocessor, preprocess
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# multimodal data and expand prompt token ids accordingly.
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# 3. Apply prompt adapter to prompt token ids if one exists.
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processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
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prompt,
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request_id=request_id,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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return_mm_hashes=self.use_hash,
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)
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eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)
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self._validate_model_inputs(processed_inputs, lora_request)
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if is_encoder_decoder_inputs(processed_inputs):
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decoder_inputs = SingletonInputsAdapter(
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processed_inputs["decoder"])
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encoder_inputs = SingletonInputsAdapter(
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processed_inputs["encoder"])
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else:
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decoder_inputs = SingletonInputsAdapter(processed_inputs)
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encoder_inputs = None
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# TODO: Impl encoder-decoder
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if encoder_inputs is not None:
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raise NotImplementedError
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assert isinstance(params, SamplingParams)
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# TODO: can we avoid cloning here in multiproc case?
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sampling_params = params.clone()
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# If unset max tokens, then generate up to the max_model_len.
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if sampling_params.max_tokens is None:
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sampling_params.max_tokens = (self.model_config.max_model_len -
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len(decoder_inputs.prompt_token_ids))
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sampling_params.update_from_generation_config(
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self.generation_config_fields, eos_token_id)
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sampling_params.update_from_tokenizer(
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self.tokenizer.get_lora_tokenizer(lora_request))
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# Multimodal related.
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sorted_mm_inputs: Optional[list[MultiModalKwargs]] = None
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sorted_mm_positions: Optional[list[PlaceholderRange]] = None
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sorted_mm_hashes: Optional[list[str]] = None
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if (decoder_mm_inputs := decoder_inputs.multi_modal_data):
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assert isinstance(decoder_mm_inputs, MultiModalKwargs)
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# The output of merged multi-modal processor (`decoder_mm_inputs`)
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# contains the kwargs for all items from all modalities.
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# This code separates them so that there is one set of kwargs
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# per item per modality.
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individual_mm_inputs = [
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MultiModalKwargs.from_items([item])
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for modality in decoder_mm_inputs.modalities
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for item in decoder_mm_inputs.get_items(modality)
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]
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# Merge and flatten multimodal placeholders, hashes and inputs
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# from dictionaries to lists, and sort them by each item's position
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# in the input sequence.
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# NOTE: interleaved modalities are not supported.
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(
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sorted_modalities,
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sorted_mm_positions,
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sorted_mm_hashes,
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) = merge_and_sort_multimodal_metadata(
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decoder_inputs.multi_modal_placeholders,
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decoder_inputs.multi_modal_hashes if self.use_hash else None,
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)
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# NOTE: Sort multimodal inputs/kwargs ONLY IF there are multiple
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# modalities involved.
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if len(sorted_modalities) > 1:
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modality_order_dict = {
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modality: order
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for order, modality in enumerate(sorted_modalities)
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}
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# Sanity check to make sure each multimodal input has only one
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# modality key.
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for mm_input in individual_mm_inputs:
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assert len(mm_input.modalities) == 1
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# Sort MultiModalKwargs to match sorted_mm_positions
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sorted_mm_inputs = sorted(
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individual_mm_inputs,
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key=lambda mm_input: modality_order_dict[list(
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mm_input.modalities)[0]])
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else:
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sorted_mm_inputs = individual_mm_inputs
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return EngineCoreRequest(
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request_id=request_id,
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prompt=decoder_inputs.prompt,
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prompt_token_ids=decoder_inputs.prompt_token_ids,
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mm_inputs=sorted_mm_inputs,
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mm_hashes=sorted_mm_hashes,
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mm_placeholders=sorted_mm_positions,
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sampling_params=sampling_params,
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eos_token_id=eos_token_id,
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arrival_time=arrival_time,
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lora_request=lora_request,
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)
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def _validate_model_inputs(self,
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inputs: ProcessorInputs,
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lora_request: Optional[LoRARequest] = None):
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if is_encoder_decoder_inputs(inputs):
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# For encoder-decoder multimodal models, the max_prompt_len
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# restricts the decoder prompt length
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prompt_inputs = inputs["decoder" if self.model_config.
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is_multimodal_model else "encoder"]
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else:
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prompt_inputs = inputs
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prompt_ids = SingletonInputsAdapter(prompt_inputs).prompt_token_ids
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if prompt_ids is None or len(prompt_ids) == 0:
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raise ValueError("Prompt cannot be empty")
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max_input_id = max(prompt_ids)
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max_allowed = self.tokenizer.get_lora_tokenizer(
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lora_request).max_token_id
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if max_input_id > max_allowed:
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raise ValueError(
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"Token id {} is out of vocabulary".format(max_input_id))
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if len(prompt_ids) >= self.model_config.max_model_len:
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raise ValueError(
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f"Prompt length of {len(prompt_ids)} is longer than the "
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f"maximum model length of {self.model_config.max_model_len}.")
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if self.model_config.is_multimodal_model:
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max_prompt_len = self.model_config.max_model_len
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if len(prompt_ids) > max_prompt_len:
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raise ValueError(
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f"The prompt (total length {len(prompt_ids)}) is too long "
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f"to fit into the model (context length {max_prompt_len}). "
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"Make sure that `max_model_len` is no smaller than the "
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"number of text tokens plus multimodal tokens. For image "
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"inputs, the number of image tokens depends on the number "
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"of images, and possibly their aspect ratios as well.")
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# TODO: Find out how many placeholder tokens are there so we can
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# check that chunked prefill does not truncate them
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# max_batch_len = self.scheduler_config.max_num_batched_tokens
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